Network intrusion detection system: machine learning approach

Abstract

The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.

Robust genetic machine learning ensemble model for intrusion detection in network traffic

“…Filtering techniques eliminate the least intriguing factors. Their primary function is as a pre-processing technique 47 . One more is the evaluation of subsets of variables by wrapper methods, which, in contrast to filter approaches, enables the identification of potential interactions between variables 48 .…”

Comparing machine learning and deep learning classifiers for enhancing agricultural productivity: case study in Larache Province, Northern Morocco

“…Machine learning is a subset of artificial intelligence methods that allows a system to analyze data and learn from it. For this reason, Machine learning algorithms are widely used for various complex problems (e.g., classification, diagnosis, prediction, and so on) compared to traditional techniques [13], [14]. Deep learning is a subset area of machine learning that can also be referred to as deep neural networks (DNN).…”

Novel Class Probability Features for Optimizing Network Attack Detection With Machine Learning

“…Performance Score [14] 2018 CICIDS2017 K Nearest Neighbours 97% [12] 2019 CICIDS2017 Bayesian Approach 98% [13] 2019 CICIDS2017 AdaBoost classifier 90% [10] 2020 CICIDS2017 Artificial Neural Network (ANN) 96% [11] 2020 CICIDS2017 Local Outlier Factor (LOF) 90% [15] 2021 CICIDS2017 Random Forest 87% [16] 2020 CICIDS2017 Decision Tree 94% [17] 2022 CICIDS2017 Convolution Neural Network 98% [18] 2023 CICIDS2017 Combination of CNN-GRU 98% [19] 2023 CICIDS2017 Random forest 98%…”

“…High f1-score performance was continuously demonstrated by recurrent neural networks and random forest models, with macro f1-scores of 0.73 and 0.87 for the CICIDS 2017 dataset and 0.73 and 0.72 for the CICIDS 2018 dataset, respectively. In this research [16], the authors proposed a machine learning-based intrusion detection and classification model. The model utilizes the KNIME analytics platform to obtain and transform the dataset, perform feature selection, and process the refined data.…”